@inproceedings{beltagy-etal-2020-arabic,
title = "{A}rabic Dialect Identification Using {BERT}-Based Domain Adaptation",
author = "Beltagy, Ahmad and
Abouelenin, Abdelrahman and
ElSherief, Omar",
editor = "Zitouni, Imed and
Abdul-Mageed, Muhammad and
Bouamor, Houda and
Bougares, Fethi and
El-Haj, Mahmoud and
Tomeh, Nadi and
Zaghouani, Wajdi",
booktitle = "Proceedings of the Fifth Arabic Natural Language Processing Workshop",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wanlp-1.26/",
pages = "262--267",
abstract = "Arabic is one of the most important and growing languages in the world. With the rise of the social media giants like Twitter, Arabic spoken dialects have become more in use. In this paper we describe our effort and simple approach on the NADI Shared Task 1 that requires us to build a system to differentiate between different 21 Arabic dialects, we introduce a deep learning semisupervised fashion approach along with pre-processing that was reported on NADI shared Task 1 Corpus. Our system ranks 4th in NADI`s shared task competition achieving 23.09{\%} F1 macro average score with a very simple yet an efficient approach on differentiating between 21 Arabic Dialects given tweets."
}
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<abstract>Arabic is one of the most important and growing languages in the world. With the rise of the social media giants like Twitter, Arabic spoken dialects have become more in use. In this paper we describe our effort and simple approach on the NADI Shared Task 1 that requires us to build a system to differentiate between different 21 Arabic dialects, we introduce a deep learning semisupervised fashion approach along with pre-processing that was reported on NADI shared Task 1 Corpus. Our system ranks 4th in NADI‘s shared task competition achieving 23.09% F1 macro average score with a very simple yet an efficient approach on differentiating between 21 Arabic Dialects given tweets.</abstract>
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%0 Conference Proceedings
%T Arabic Dialect Identification Using BERT-Based Domain Adaptation
%A Beltagy, Ahmad
%A Abouelenin, Abdelrahman
%A ElSherief, Omar
%Y Zitouni, Imed
%Y Abdul-Mageed, Muhammad
%Y Bouamor, Houda
%Y Bougares, Fethi
%Y El-Haj, Mahmoud
%Y Tomeh, Nadi
%Y Zaghouani, Wajdi
%S Proceedings of the Fifth Arabic Natural Language Processing Workshop
%D 2020
%8 December
%I Association for Computational Linguistics
%C Barcelona, Spain (Online)
%F beltagy-etal-2020-arabic
%X Arabic is one of the most important and growing languages in the world. With the rise of the social media giants like Twitter, Arabic spoken dialects have become more in use. In this paper we describe our effort and simple approach on the NADI Shared Task 1 that requires us to build a system to differentiate between different 21 Arabic dialects, we introduce a deep learning semisupervised fashion approach along with pre-processing that was reported on NADI shared Task 1 Corpus. Our system ranks 4th in NADI‘s shared task competition achieving 23.09% F1 macro average score with a very simple yet an efficient approach on differentiating between 21 Arabic Dialects given tweets.
%U https://aclanthology.org/2020.wanlp-1.26/
%P 262-267
Markdown (Informal)
[Arabic Dialect Identification Using BERT-Based Domain Adaptation](https://aclanthology.org/2020.wanlp-1.26/) (Beltagy et al., WANLP 2020)
ACL